package cn._51doit.flink.day08;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;

/**
 * 使用定时器实现类似滚动窗口增量聚合的功能
 *
 *  *
 *  * 1.每个key都可以注册自己独立的定时器，定时器触发会调用onTimer，并且将对应的Key传入到OnTimerContext
 *  * 2.每个key多可以注册多个定时器，按照注册时间的先后顺序依次触发（如果同一个key注册了多个相同时间的定时器，只会触发一个）
 *  *
 */

public class ProcessFunctionDemo4 {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //开启checkpoint
        env.enableCheckpointing(5000);
        //设置重启策略
        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(5, 5000));

        DataStreamSource<String> lines = env.socketTextStream("localhost", 8888);

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
            @Override
            public void flatMap(String line, Collector<Tuple2<String, Integer>> out) throws Exception {
                if (line.startsWith("error")) {
                    throw new RuntimeException("有错误数据出现，抛出异常！");
                }
                String[] words = line.split(" ");
                for (String word : words) {
                    out.collect(Tuple2.of(word, 1));
                }
            }
        });

        KeyedStream<Tuple2<String, Integer>, String> keyedStream = wordAndOne.keyBy(t -> t.f0);

        SingleOutputStreamOperator<Tuple2<String, Integer>> res = keyedStream.process(new KeyedProcessFunction<String, Tuple2<String, Integer>, Tuple2<String, Integer>>() {

            private transient ValueState<Integer> valueState;
            @Override
            public void open(Configuration parameters) throws Exception {
                //定义状态描述器(描述状态的类型、名称)
                ValueStateDescriptor<Integer> stateDescriptor = new ValueStateDescriptor<>("wc-state", Integer.class);
                //初始化或恢复状态
                valueState = getRuntimeContext().getState(stateDescriptor);
            }

            @Override
            public void processElement(Tuple2<String, Integer> input, Context ctx, Collector<Tuple2<String, Integer>> out) throws Exception {

                Integer current = input.f1;
                Integer history = valueState.value();
                if (history == null) {
                    history = 0;
                }
                current += history;
                //更新状态
                valueState.update(current);

                int windowSize = 30000;
                long triggerTime = System.currentTimeMillis() - System.currentTimeMillis() % windowSize + windowSize;
                ctx.timerService().registerProcessingTimeTimer(triggerTime);

                //不输出数据
            }

            @Override
            public void onTimer(long timestamp, OnTimerContext ctx, Collector<Tuple2<String, Integer>> out) throws Exception {

                out.collect(Tuple2.of(ctx.getCurrentKey(), valueState.value()));
                valueState.clear();
            }
        });

        res.print();

        env.execute();


    }




}
